Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates
The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse ga...
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description | The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by a physically-based mechanistic model. This explicitly accounts for four pools of organic polymers, seven pools of organic monomers, five microbial functional groups, and inorganic N and C species. We first benchmarked our model against vertical SOM profiles measured in a temperate forest in North-Eastern Bavaria, Germany (Staudt and Foken in Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology, 2007). Next, we conducted a sensitivity analysis to biogeochemical parameters using modified Morris indices for target SOM pools and gas emissions from a tropical, a temperate, and a semi-arid grassland in Australia. We found that greenhouse gas emissions, the SOM stock, and the fungi-to-bacteria ratio in the top soil were more sensitive to the mortality of aerobic bacteria than other biogeochemical parameters. The larger
CO
2
emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands. |
doi_str_mv | 10.1007/s00477-020-01868-z |
format | Article |
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CO
2
emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-020-01868-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Aerobic bacteria ; Aquatic Pollution ; Aridity ; Atmospheric models ; Bacteria ; Biogeochemistry ; Carbon dioxide ; Carbon dioxide emissions ; Chemistry and Earth Sciences ; Climate prediction ; Computational Intelligence ; Computer Science ; Earth and Environmental Science ; Earth Sciences ; Emissions ; Environment ; Environment models ; Environmental modeling ; Environmental research ; Functional groups ; Fungi ; Grasslands ; Greenhouse effect ; Greenhouse gases ; Math. Appl. in Environmental Science ; Micrometeorology ; Microorganisms ; Monomers ; Organic matter ; Organic soils ; Original Paper ; Parameter identification ; Parameter modification ; Parameter sensitivity ; Physics ; Polymers ; Pools ; Probability Theory and Stochastic Processes ; Sensitivity analysis ; Soil dynamics ; Soil organic matter ; Soils ; Statistics for Engineering ; Temperate forests ; Vegetation ; Waste Water Technology ; Water Management ; Water Pollution Control</subject><ispartof>Stochastic environmental research and risk assessment, 2020-12, Vol.34 (12), p.2229-2244</ispartof><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020</rights><rights>Springer-Verlag GmbH Germany, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-81f09724591f6ae2d3842df7eb33f49564083ae2786987ec55ffea745322637c3</citedby><cites>FETCH-LOGICAL-c319t-81f09724591f6ae2d3842df7eb33f49564083ae2786987ec55ffea745322637c3</cites><orcidid>0000-0002-4998-7921</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-020-01868-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-020-01868-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Ceriotti, G.</creatorcontrib><creatorcontrib>Tang, F. H. M.</creatorcontrib><creatorcontrib>Maggi, F.</creatorcontrib><title>Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><description>The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by a physically-based mechanistic model. This explicitly accounts for four pools of organic polymers, seven pools of organic monomers, five microbial functional groups, and inorganic N and C species. We first benchmarked our model against vertical SOM profiles measured in a temperate forest in North-Eastern Bavaria, Germany (Staudt and Foken in Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology, 2007). Next, we conducted a sensitivity analysis to biogeochemical parameters using modified Morris indices for target SOM pools and gas emissions from a tropical, a temperate, and a semi-arid grassland in Australia. We found that greenhouse gas emissions, the SOM stock, and the fungi-to-bacteria ratio in the top soil were more sensitive to the mortality of aerobic bacteria than other biogeochemical parameters. The larger
CO
2
emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands.</description><subject>Aerobic bacteria</subject><subject>Aquatic Pollution</subject><subject>Aridity</subject><subject>Atmospheric models</subject><subject>Bacteria</subject><subject>Biogeochemistry</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide emissions</subject><subject>Chemistry and Earth Sciences</subject><subject>Climate prediction</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Emissions</subject><subject>Environment</subject><subject>Environment models</subject><subject>Environmental modeling</subject><subject>Environmental research</subject><subject>Functional groups</subject><subject>Fungi</subject><subject>Grasslands</subject><subject>Greenhouse effect</subject><subject>Greenhouse gases</subject><subject>Math. Appl. in Environmental Science</subject><subject>Micrometeorology</subject><subject>Microorganisms</subject><subject>Monomers</subject><subject>Organic matter</subject><subject>Organic soils</subject><subject>Original Paper</subject><subject>Parameter identification</subject><subject>Parameter modification</subject><subject>Parameter sensitivity</subject><subject>Physics</subject><subject>Polymers</subject><subject>Pools</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Sensitivity analysis</subject><subject>Soil dynamics</subject><subject>Soil organic matter</subject><subject>Soils</subject><subject>Statistics for Engineering</subject><subject>Temperate forests</subject><subject>Vegetation</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kc1KAzEUhQdRsFRfwFXAjS5G8zuZWUrxDxQX6jqkmZsamSY1SQv1ZXxVoyO6c5Wby3fOuXCq6ojgM4KxPE8YcylrTHGNSdu09ftONSGcNTWjotv9nTnerw5TcvMiEqzrCJ5UH49u6QYdXXaQkPY96p21EMGb8nce5RdACXwqwMblLQoWpeAGFOJCe2fQUucMEZ08Ptyfon7r9dKZhHJAcxcWEMwLlIUe0EpHvYSCJmRD_E3JaAMLyDq74Evcap3HK8zgijOkg2rP6iHB4c87rZ6vLp9mN_Xdw_Xt7OKuNox0uW6JxZ2kXHTENhpoz1pOeythzpjlnWg4blnZy7bpWglGiBKvJReM0oZJw6bV8ei7iuFtDSmr17COvkQqyiURoiWCFYqOlIkhpQhWrWK5M24VweqrCzV2oUoX6rsL9V5EbBSlAvsFxD_rf1SfgACPzA</recordid><startdate>20201201</startdate><enddate>20201201</enddate><creator>Ceriotti, G.</creator><creator>Tang, F. 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M.</creator><creator>Maggi, F.</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0002-4998-7921</orcidid></search><sort><creationdate>20201201</creationdate><title>Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates</title><author>Ceriotti, G. ; Tang, F. H. M. ; Maggi, F.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-81f09724591f6ae2d3842df7eb33f49564083ae2786987ec55ffea745322637c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aerobic bacteria</topic><topic>Aquatic Pollution</topic><topic>Aridity</topic><topic>Atmospheric models</topic><topic>Bacteria</topic><topic>Biogeochemistry</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide emissions</topic><topic>Chemistry and Earth Sciences</topic><topic>Climate prediction</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Emissions</topic><topic>Environment</topic><topic>Environment models</topic><topic>Environmental modeling</topic><topic>Environmental research</topic><topic>Functional groups</topic><topic>Fungi</topic><topic>Grasslands</topic><topic>Greenhouse effect</topic><topic>Greenhouse gases</topic><topic>Math. Appl. in Environmental Science</topic><topic>Micrometeorology</topic><topic>Microorganisms</topic><topic>Monomers</topic><topic>Organic matter</topic><topic>Organic soils</topic><topic>Original Paper</topic><topic>Parameter identification</topic><topic>Parameter modification</topic><topic>Parameter sensitivity</topic><topic>Physics</topic><topic>Polymers</topic><topic>Pools</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Sensitivity analysis</topic><topic>Soil dynamics</topic><topic>Soil organic matter</topic><topic>Soils</topic><topic>Statistics for Engineering</topic><topic>Temperate forests</topic><topic>Vegetation</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ceriotti, G.</creatorcontrib><creatorcontrib>Tang, F. H. M.</creatorcontrib><creatorcontrib>Maggi, F.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ceriotti, G.</au><au>Tang, F. H. M.</au><au>Maggi, F.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2020-12-01</date><risdate>2020</risdate><volume>34</volume><issue>12</issue><spage>2229</spage><epage>2244</epage><pages>2229-2244</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by a physically-based mechanistic model. This explicitly accounts for four pools of organic polymers, seven pools of organic monomers, five microbial functional groups, and inorganic N and C species. We first benchmarked our model against vertical SOM profiles measured in a temperate forest in North-Eastern Bavaria, Germany (Staudt and Foken in Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology, 2007). Next, we conducted a sensitivity analysis to biogeochemical parameters using modified Morris indices for target SOM pools and gas emissions from a tropical, a temperate, and a semi-arid grassland in Australia. We found that greenhouse gas emissions, the SOM stock, and the fungi-to-bacteria ratio in the top soil were more sensitive to the mortality of aerobic bacteria than other biogeochemical parameters. The larger
CO
2
emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00477-020-01868-z</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0002-4998-7921</orcidid></addata></record> |
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subjects | Aerobic bacteria Aquatic Pollution Aridity Atmospheric models Bacteria Biogeochemistry Carbon dioxide Carbon dioxide emissions Chemistry and Earth Sciences Climate prediction Computational Intelligence Computer Science Earth and Environmental Science Earth Sciences Emissions Environment Environment models Environmental modeling Environmental research Functional groups Fungi Grasslands Greenhouse effect Greenhouse gases Math. Appl. in Environmental Science Micrometeorology Microorganisms Monomers Organic matter Organic soils Original Paper Parameter identification Parameter modification Parameter sensitivity Physics Polymers Pools Probability Theory and Stochastic Processes Sensitivity analysis Soil dynamics Soil organic matter Soils Statistics for Engineering Temperate forests Vegetation Waste Water Technology Water Management Water Pollution Control |
title | Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates |
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